容错
计算机科学
卡尔曼滤波器
吞吐量
惯性导航系统
实时计算
扩展卡尔曼滤波器
滤波器(信号处理)
分布式计算
数据挖掘
人工智能
惯性参考系
计算机视觉
电信
无线
物理
量子力学
标识
DOI:10.1109/plans.1988.195473
摘要
An efficient, federated Kalman filtering method is presented, based on rigorous information-sharing principles. The method applies to decentralized navigation systems in which one or more sensor-dedicated local filters feed a larger master filter. The local filters operate in parallel, processing unique data from their local sensors, and common data from a shared inertial navigation system. The master filter combines local filter outputs at a selectable reduced rate, and yields estimates that are globally optimal or subset-optimal. The method provides major improvements in throughput (speed) and fault tolerance, and is well suited to real-time implementation. Practical federated filter examples are presented, and discussed in terms of structure, accuracy, fault tolerance, throughput, data compression, and other real-time issues.< >
科研通智能强力驱动
Strongly Powered by AbleSci AI